Development and validation of a predictive model for American Society of Anesthesiologists Physical Status

Seshadri C Mudumbai,Mary T Hawn,Andrea K Finlay,Suzann Pershing,Robin N Kamal,Amber W Trickey,Thomas Bowe,Edward R Mariano,Erika D Sears,Yingjie Weng,Alex H S Harris,Dan Eisenberg

doi:10.1186/s12913-019-4640-x

Abstract

BackgroundThe American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes.MethodsUsing the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator.ResultsOf the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/− 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ConclusionsModel-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

Highlights

The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery
ASA-PS is included within riskadjustment algorithms comparing hospital performance in surgical care i.e., the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) [10]
Development of analytic sample From the 750,937 surgeries represented in the 2014 Participant Use File (PUF)(Fig. 1), we excluded procedures that met any of the following criteria: emergent or non-elective surgeries; surgeries for patients transferred to the hospital; patients < 18 years old or ≥ 90 years old at the time of surgery; procedures for patients who were inpatients immediately prior to their index surgery; surgeries for patients who had missing ASA assignment or had a ASA-PS class of ‘5-Moribund’; and procedures of patients who had incomplete data for any of the predictors identified for model development

Summary

Introduction

The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. The American Society of Anesthesiologists Physical Status Classification system (ASA-PS) is a commonly used, subjective method to categorize patients’ fitness for surgery [1, 2]. Developed by Saklad et al, the sixpoint classification system ranges from healthy patients. While other and potentially better surgical outcome prediction methods are available, most have. The ASA-PS has face validity as an assessment of functional capacity, which is increasingly thought to be a significant predictor of patient outcome [9]. ASA-PS is included within riskadjustment algorithms comparing hospital performance in surgical care i.e., the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) [10]. Models drawn entirely from preoperative NSQIP data may be helpful in risk stratification during the preoperative evaluation process [7, 11, 12]

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